Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction

TL;DR

This study compares six machine learning models to predict myocardial infarction, finding XGBoost as the most accurate, thereby highlighting the potential of advanced algorithms to improve cardiovascular disease prediction.

Contribution

It provides a comparative analysis of multiple machine learning models specifically for myocardial infarction prediction, identifying the most effective approach.

Findings

XGBoost achieved the highest accuracy at 92.72%

Support Vector Machine had 75.01% accuracy

Machine learning models can significantly improve prediction accuracy for cardiovascular diseases

Abstract

Cardiovascular disease remains a leading cause of mortality in the contemporary world. Its association with smoking, elevated blood pressure, and cholesterol levels underscores the significance of these risk factors. This study addresses the challenge of predicting myocardial illness, a formidable task in medical research. Accurate predictions are pivotal for refining healthcare strategies. This investigation conducts a comparative analysis of six distinct machine learning models: Logistic Regression, Support Vector Machine, Decision Tree, Bagging, XGBoost, and LightGBM. The attained outcomes exhibit promise, with accuracy rates as follows: Logistic Regression (81.00%), Support Vector Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), Decision Tree (82.30%), and Bagging (83.01%). Notably, XGBoost emerges as the top-performing model. These findings underscore its potential to enhance predictive precision for coronary infarction. As the prevalence of cardiovascular risk factors persists, incorporating advanced machine learning techniques holds the potential to refine proactive medical interventions.